Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved ...Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved in goal-directed behavior. Extensive research has been done to develop methods for recognizing physiological and psychophysiological signs of mental fatigue. This has allowed the development of many AI-based models to classify different levels of fatigue, using data extracted from eye-tracking device, EEG, or ECG. In this paper, we present an experimental protocol which aims to both generate/measure mental fatigue and provide effective strategies for recuperation via VR sessions paired with EEG and eye tracking devices. This paper first provides a comprehensive state-of-the-art of mental fatigue predictive factors, measurement methods, and recuperation strategies. Then the paper presents an experimental protocol resulting from the state-of-the-art to 1) generate and measure mental fatigue and 2) evaluate the effectiveness of virtual therapy for fatigue recuperation, using a virtual reality (VR) simulated environment. In our work, we successfully generated mental fatigue through completion of cognitive tasks in a virtual simulated environment. Participants showed significant decline in pupil diameter and theta/alpha score during the various cognitive tasks. We trained an RBF SVM classifier from Electroencephalogram (EEG) data classifying mental fatigue with 95% accuracy on the test set. Finally, our results show that the time allocated for virtual therapy did not improve pupil diameter in post-relaxation period. Further research on the impact of relaxation therapy on relaxation therapy should allocate time closer to the standard recovery time of 60 min.展开更多
This paper aims to conduct a comprehensive exergoeconomic analysis of a novel zero-carbon-emission multi-generation system and propose a fast optimization method combined with machine learning.The detailed exergoecono...This paper aims to conduct a comprehensive exergoeconomic analysis of a novel zero-carbon-emission multi-generation system and propose a fast optimization method combined with machine learning.The detailed exergoeconomic analysis of a novel combined power,freshwater and cooling multi-generation system is performed in this study.The exergoeconomic analysis model is established by exergy flow theory.A comprehensive exergy,exergoeconomic and environmental analysis is carried out.Five critical decision variables are researched to bring out effects on the multi-generation system exergoeconomic performance.A novel fast optimization method combining genetic algorithm and Bagging neural network is proposed.The advanced nature comparison is made between the proposed system and four similar cases.Results display that increasing the turbine inlet temperature can improve exergy efficiency and decrease the total product unit cost.The multi-generation system exergy destruction directly determines exergy efficiency and total exergy destruction cost rate.The total product unit cost in the cost optimal design case is reduced by 7.7%and 25%,respectively,compared with exergy efficiency optimal design case and basic design case.Compared with four similar cases,the proposed multi-generation system has great advantages in thermodynamic performance and exergoeconomic performance.This paper can provide research methods and ideas for performance analysis and fast optimization of multi-generation system.展开更多
Artificial intelligence(AI)and machine learning(ML)are powerful technologies with the potential to revolutionize motor recovery in rehabilitation medicine.This perspective explores how AI and ML are harnessed to asses...Artificial intelligence(AI)and machine learning(ML)are powerful technologies with the potential to revolutionize motor recovery in rehabilitation medicine.This perspective explores how AI and ML are harnessed to assess,diagnose,and design personalized treatment plans for patients with motor impairments.The integration of wearable sensors,virtual reality,augmented reality,and robotic devices allows for precise movement analysis and adaptive neurorehabilitation approaches.Moreover,AI-driven telerehabilitation enables remote monitoring and consultation.Although these applications show promise,healthcare professionals must interpret AI-generated insights and ensure patient safety.While AI and ML are in their early stages,ongoing research will determine their effectiveness in rehabilitation medicine.展开更多
文摘Mental fatigue is a complex state that results from prolonged cognitive activity. Symptoms of mental fatigue can include change in mood, motivation, and temporary deterioration of various cognitive functions involved in goal-directed behavior. Extensive research has been done to develop methods for recognizing physiological and psychophysiological signs of mental fatigue. This has allowed the development of many AI-based models to classify different levels of fatigue, using data extracted from eye-tracking device, EEG, or ECG. In this paper, we present an experimental protocol which aims to both generate/measure mental fatigue and provide effective strategies for recuperation via VR sessions paired with EEG and eye tracking devices. This paper first provides a comprehensive state-of-the-art of mental fatigue predictive factors, measurement methods, and recuperation strategies. Then the paper presents an experimental protocol resulting from the state-of-the-art to 1) generate and measure mental fatigue and 2) evaluate the effectiveness of virtual therapy for fatigue recuperation, using a virtual reality (VR) simulated environment. In our work, we successfully generated mental fatigue through completion of cognitive tasks in a virtual simulated environment. Participants showed significant decline in pupil diameter and theta/alpha score during the various cognitive tasks. We trained an RBF SVM classifier from Electroencephalogram (EEG) data classifying mental fatigue with 95% accuracy on the test set. Finally, our results show that the time allocated for virtual therapy did not improve pupil diameter in post-relaxation period. Further research on the impact of relaxation therapy on relaxation therapy should allocate time closer to the standard recovery time of 60 min.
基金financial support from the Jilin provincial Development and Reform Commission(No.2023C032-7)Science Foundation of Jilin province Science and Technology Agency(No.20210203057SF)Science and Technology Development Program of Jilin province Science and Technology Agency(No.20230101211JC)。
文摘This paper aims to conduct a comprehensive exergoeconomic analysis of a novel zero-carbon-emission multi-generation system and propose a fast optimization method combined with machine learning.The detailed exergoeconomic analysis of a novel combined power,freshwater and cooling multi-generation system is performed in this study.The exergoeconomic analysis model is established by exergy flow theory.A comprehensive exergy,exergoeconomic and environmental analysis is carried out.Five critical decision variables are researched to bring out effects on the multi-generation system exergoeconomic performance.A novel fast optimization method combining genetic algorithm and Bagging neural network is proposed.The advanced nature comparison is made between the proposed system and four similar cases.Results display that increasing the turbine inlet temperature can improve exergy efficiency and decrease the total product unit cost.The multi-generation system exergy destruction directly determines exergy efficiency and total exergy destruction cost rate.The total product unit cost in the cost optimal design case is reduced by 7.7%and 25%,respectively,compared with exergy efficiency optimal design case and basic design case.Compared with four similar cases,the proposed multi-generation system has great advantages in thermodynamic performance and exergoeconomic performance.This paper can provide research methods and ideas for performance analysis and fast optimization of multi-generation system.
文摘Artificial intelligence(AI)and machine learning(ML)are powerful technologies with the potential to revolutionize motor recovery in rehabilitation medicine.This perspective explores how AI and ML are harnessed to assess,diagnose,and design personalized treatment plans for patients with motor impairments.The integration of wearable sensors,virtual reality,augmented reality,and robotic devices allows for precise movement analysis and adaptive neurorehabilitation approaches.Moreover,AI-driven telerehabilitation enables remote monitoring and consultation.Although these applications show promise,healthcare professionals must interpret AI-generated insights and ensure patient safety.While AI and ML are in their early stages,ongoing research will determine their effectiveness in rehabilitation medicine.
